Artificial neural network for modeling formulation and drug permeation of topical patches containing diclofenac sodium

Lefnaoui, Sonia; Rebouh, Samia; Bouhedda, Mounir; Yahoum, Madiha Melha

doi:10.1007/s13346-019-00671-w

Cited by 19 publications

(5 citation statements)

References 61 publications

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“…Artificial neural network models are successfully applied in different domains to predict unknown parameters [ 12 , 20 , 31 ]. These models are widely explored in drug delivery to predict drug release from different drug delivery systems [ 31 , 32 , 33 ]. In our context, Razi et al [ 10 ] developed an ANN model to evaluate the apparent viscosity of xanthan gum at different temperatures and concentrations.…”

Section: Resultsmentioning

confidence: 99%

Experimental Analysis and Neural Network Modeling of the Rheological Behavior of Xanthan Gum and Its Derivatives

et al. 2023

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The main objective of this study was to create a mathematical tool that could be used with experimental data to predict the rheological flow behavior of functionalized xanthan gum according to the types of chemical groups grafted onto its backbone. Different rheological and physicochemical analyses were applied to assess six derivatives synthesized via the etherification of xanthan gum by hydrophobic benzylation with benzyl chloride and carboxymethylation with monochloroacetic acid at three (regent/polymer) ratios R equal to 2.4 and 6. Results from the FTIR study verified that xanthan gum had been modified. The degree of substitution (DS) values varying between 0.2 and 2.9 for carboxymethylxanthan gum derivatives were found to be higher than that of hydrophobically modified benzyl xanthan gum for which the DS ranged from 0.5 to 1. The molecular weights of all the derivatives were found to be less than that of xanthan gum for the two types of derivatives, decreasing further as the degree of substitution (DS) increased. However, the benzyl xanthan gum derivatives presented higher molecular weights varying between 1,373,146 (g/mol) and 1,262,227 (g/mol) than carboxymethylxanthan gum derivatives (1,326,722–1,015,544) (g/mol). A shear-thinning behavior was observed in the derivatives, and the derivatives’ viscosity was found to decrease with increasing DS. The second objective of this research was to create an ANN model to predict one of the rheological properties (the apparent viscosity). The significance of the ANN model (R2 = 0.99998 and MSE = 5.95 × 10−3) was validated by comparing experimental results with the predicted ones. The results showed that the model was an efficient tool for predicting rheological flow behavior.

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Section: Resultsmentioning

confidence: 99%

Experimental Analysis and Neural Network Modeling of the Rheological Behavior of Xanthan Gum and Its Derivatives

et al. 2023

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“…Artificial neural network (ANN)[ 58 ] was developed to forecast the release kinetic profile of drug in TDDDS. Polymer concentration, time, and carrageenan amount were the permeation governing factors and consequently cumulative amount of drug released and cumulative permeation of drug per unit surface area with respect to time were determined.…”

Section: Measurement Of Drug Permeation and Retention From Topical Dermal Dosage Forms Using Computational Techniquesmentioning

confidence: 99%

“…Moreover, ANN simultaneously demonstrated that release and diffusion mechanisms are influenced by the formulation parameters. [ 58 ] In another experiment, a predicting model for skin permeability represented as log K p was established. A comparative evaluation was carried out between prediction and experimental results to obtain the relationship between Abraham descriptors and log K p. Multiple linear regression model was computed that demonstrated n = 215 with determination coefficient and R 2 = 0.699.…”

Section: Measurement Of Drug Permeation and Retention From Topical Dermal Dosage Forms Using Computational Techniquesmentioning

confidence: 99%

Does skin permeation kinetics influence efficacy of topical dermal drug delivery system?

Iyer

Jyothi

Agrawal

et al. 2021

Journal of Advanced Pharmaceutical Technology &Amp; Research

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Skin permeation is an integral part of penetration of topical therapeutics. Zero order in addition to Higuchi permeation kinetic is usually preferred in topical drug delivery cargo. Penetration of therapeutic entities through epidermal barrier is a major challenge for scientific fraternity. Furthermore, penetration of therapeutic entities determines the transportation and ultimately therapeutic efficacy of topical dermal dosage forms. Apart from experimentation models, mathematical equations, in silico docking, molecular dynamics (MDs), and artificial neural network (Neural) techniques are being used to assess free energies and prediction of electrostatic attractions in order to predict the permeation phenomena of therapeutic entities. Therefore, in the present review, we have summarized the significance of kinetic equations, in silico docking, MDs, and ANN in assessing and predicting the penetration behavior of topical therapeutics through dermal dosage form. In addition, the role of chitosan biomacromolecule in modulating permeation of topical therapeutics in skin has also been illustrated using computational techniques.

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“…ANNs mimic the working process of the human brain, being able to deal with large databases and allowing to establish general patterns and non-linear cause-effect relationships between inputs and outputs (Wang et al., 2006 ; Landín et al., 2009 ; Colbourn et al., 2011 ). Recently, ANNs have been successfully used for tailoring different dosage forms as nanoparticles, topical patches, tablets or hydrogels (Rouco et al., 2018 ; Lefnaoui et al., 2020 ; Simões et al., 2020 ; Garcia-del Rio et al., 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Rationalized design to explore the full potential of PLGA microspheres as drug delivery systems

Martinez-Borrajo,

Diaz-Rodriguez,

Landin

2023

Drug Delivery

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Polymeric microparticles are widely used as drug delivery platforms either alone or embedded in more complex structures for regenerative medicine. Emulsion-solvent evaporation is the most extensively used technique for microparticles preparation. Despite the apparent simplicity of this method, there is no general procedure for producing microparticles of predictable characteristics (particle size, size distribution, encapsulation efficiency, and drug loading). Hybrid systems such as neurofuzzy logic allow identifying relationships between inputs and outputs, expressing the generated mathematical models through rules in linguistic format. In this work, the relationships between the variables involved in the emulsion-solvent evaporation process and the quality parameters of PLGA microparticles as drug delivery systems were established. Neurofuzzy logic software was able to generate models of high predictability (> 85%) for the microspheres properties namely particle size, size distribution, encapsulation efficiency and drug loading. Moreover, the generated sets of IF-THEN rules allowed to dictate general guidelines to better select the PLGA microparticles formulation parameters. This approach would be of great interest as a starting point to set-up protocols for the development of PLGA microparticles obtained by emulsion-solvent evaporation for many applications.

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Artificial neural network for modeling formulation and drug permeation of topical patches containing diclofenac sodium

Cited by 19 publications

References 61 publications

Experimental Analysis and Neural Network Modeling of the Rheological Behavior of Xanthan Gum and Its Derivatives

Experimental Analysis and Neural Network Modeling of the Rheological Behavior of Xanthan Gum and Its Derivatives

Does skin permeation kinetics influence efficacy of topical dermal drug delivery system?

Rationalized design to explore the full potential of PLGA microspheres as drug delivery systems

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